Suzuki, S., Saeki, M., & Matsuura, R. (2023, June). Pedagogical potential of multimodal conversational AI for foreign language learning and assessment. Paper presented at Korean PhD students and researchers in the UK (KRUK) (Zoom).

Suzuki, S., Saeki, M., & Matsuura, R. (2022, July). Development of Online Language Learning Assistant AI System that Grows with Humans. International Collaborative Practice S V: Computer-Assisted Language Learning, University of Tokyo.

鈴木駿吾, 英会話学習におけるInteLLAの可能性, ワールド・ファミリー バイリンガル サイエンス研究所第1回メディア向けセミナー「最先端のテクノロジーを活用した英語教育 〜研究者・教師・メディアが共に考える会〜」, Dec, 2022

鈴木駿吾, 第二言語スピーキング能力とその評価：Is technology a friend or foe?, 中央大学国際情報学部, Nov, 2022

松山洋一，人とAIの共進化と融合　～英語教育での先端実践事例から語る～，オンラインラーニングフォーラム2022，Nov. 2022.

松山洋一，鈴木駿吾，人と共に成長するオンライン語学学習支援AIシステム InteLLA，ワールド・ファミリー バイリンガル サイエンス研究所第1回メディア向けセミナー「VRやAIを活用した最先端の英語学習法」，June. 2022.

Shungo Suzuki, The role of creativity in L2 speech production: The importance of both cognitive and social-personality approaches, International Online Symposium on Individual Differences and Creativity in L2 learning, Universidad de La Rioja, Spain, June. 2022．

松山洋一，英語学習の未来 〜メタバース時代の会話AI技術の可能性を語る〜，日本英語コーチング協会 JELCAシンポジウム「テクノロジーが拓く英語コーチングの未来」，Mar. 2022.

Shungo Suzuki and Ryuki Matsuura, L2 oral fluency: From the construct definition to automated scoring,
Applied Linguistics Research Circle Weekly Talk, Department of English Language and Applied Linguistics, University of Reading, Feb. 2022.

松山洋一，人と共に成長する英会話能力判定エージェントの開発，日本音響学会2022年春季研究発表会スペシャルセッション「教育支援のための音声処理技術」，Mar. 2022.

Suzuki, S. L2 oral fluency: Towards bridging a gap between SLA research and language testing, the Japan Association for Language Education and Technology, Methodology Special Interest Group, November 2021.

松山洋一，”Tutorial English AI：人と共に進化する会話AIの実現に向かって“, NTTメディアインテリジェンス研究所, March 2021.

緒方淳，松山洋一，“人と共に進化する次世代人工知能に関する技術開発事業”,  情報処理学会研究報告 (SLP)，Feb. 2021.

Zhu, H., Dai, D. W., Brandt, A., Chen, G., Ferri, G., Hazel, S., Jenks, C., & Jones, R. (2024). Exploring AI for intercultural communication : open conversation. Applied Linguistics Review, 1–16. https://doi.org/10.1515/applirev-2024-0186

Dai, D. W., Suzuki, S., & Chen, G. (2024). Generative AI for professional communication training in intercultural contexts: where are we now and where are we heading? Applied Linguistics Review, 1–12. https://doi.org/10.1515/applirev-2024-0184

Eguchi, M. (2022). Modeling lexical and phraseological sophistication in oral proficiency interviews: A conceptual replication. Vocabulary Learning and Instruction, 11(2), 1–16. https://doi.org/10.7820/vli.v11. 2.Eguchi

Kurata, F., Saeki, M., Eguchi, M., Suzuki, S., Takatsu, H., & Matsuyama Y. (2024). Development and Validation of Engagement and Rapport Scales for Evaluating User Experience in Multimodal Dialogue Systems. 14th International Workshop on Spoken Dialog System Technology (IWSDS 2024), Sapporo, Japan.

Kurata, F., Saeki, M., Shinya F., & Yoichi M. (2023). Multimodal Turn-Taking Model Using Visual Cues for End-of-Utterance Prediction in Spoken Dialogue Systems. Proc. The 24th Annual Conference of the International Speech Communication Association (INTERSPEECH2023), Dublin, Ireland.

Suzuki, S., Matsuura, R., Inada, Y., Saeki, M., & Matsuyama, Y. (2023, July). What temporal and dialogic features distinguish between second language oral proficiency levels? The case of oral proficiency interview. In P. Peltonen. & C. Wright (Conveners), Fluency as a multilingual practice: Concepts and challenges. Colloquium conducted at the 20th Annual Congress of International Association of Applied Linguistics (AILA), University of Lyon, France.

Matsuura, R., & Suzuki, S. (2023). Prompt-independent automated scoring of L2 oral fluency by capturing prompt effects. The 24th International Conference on Artificial Intelligence in Education (AIED 2023), Tokyo, Japan.

Matsuura, R. (2023, June). Developing and validating automatic annotation system of silent pause locations and disfluency words. Language Testing Research Colloquium (LTRC 2023) Poster.

Yoichi Matsuyama, Shungo Suzuki, Mao Saeki, Hiroaki Takatsu, Ryuki Matsuura, Yuya Arai, (2023, June). Towards an explainable automated scoring of spoken interaction with a conversational AI agent. Language Testing Research Colloquium (LTRC 2023) Research paper in Symposium.

Takizawa, K., Kiyota, A., Suzuki, S., Sawaki, Y., Matsumura, K., Oi, Y., Deng, Y. (2023, June). Developing an interactional competence rating scale for a university speaking placement test: Insights from existing rating scales and performance data. Language Testing Research Colloquium (LTRC 2023) Works-in-Progress.

Suzuki, S., Tanaka, R., Takizawa, K., Eguchi, M., Saeki, M., Matsuyama, Y. (2023, June). Linguistic, discourse and functional aspects of interactional competence across proficiency levels: The case of paired oral discussion task. Language Testing Research Colloquium (LTRC 2023) Research Paper.

Mao S., Kotoka M., Shinya F., Shungo S., Tetsuji O., Tetsunori K., Yoichi M. (2022). Confusion detection for adaptive conversational strategies of an oral proficiency assessment interview agent. Proc. The 23rd Annual Conference of the International Speech Communication Association (INTERSPEECH2022), Incheon, Korea.

Suzuki, S., Matsuura, R., Saeki, M., & Matsuyama, Y. What temporal features distinguish between second language oral proficiency levels? The case of Japanese learners of English, the 31st annual conference of the European Second Language Association (EUROSLA 2022), Research paper.

Suzuki, S., Matsuura, R., Saeki, M., & Matsuyama, Y. How is dialogic fluency different from monologic fluency? The case of oral proficiency interview, the 9th International Conference on Task-based language teaching (TBLT 2022), Research paper.

Takagi, R., Suzuki, S., Saeki, M., & Matsuyama, Y. Balancing interactional authenticity and variability in the assessment of interactional competence: A comparative study of human interlocutors and conversational virtual agent, Language Testing Research Colloquium (LTRC 2022) Work-in-progress.

Suzuki, S., Matsuura, R., Saeki, M., & Matsuyama, Y. Revisiting the assessment potential of read-aloud speech performance: Cognitive validity and predictive validity, Language Testing Research Colloquium (LTRC 2022) Research paper.

Mao Saeki, Weronika Demkow, Tetsunori Kobayashi, and Yoichi Matsuyama, “A WoZ Study for an Incremental Proficiency Scoring Interview Agent Eliciting Ratable Samples“, 12th International Workshop on Spoken Dialog System Technology (IWSDS 2021).

Mao Saeki, Yoichi Matsuyama, Satoshi Kobashikawa, Tetsuji Ogawa, Tetsunori Kobayashi, “Analysis of Multimodal Features for Speaking Proficiency Scoring in An Interview Dialogue,” Proc. The 8th IEEE Spoken Language Technology Workshop (SLT2021), pp.629-635, Jan. 2021.

倉田 楓真，佐伯 真於，藤江 真也，松山 洋一，視線・口・頭部の動作特徴量に着目したマルチモーダル発話終了予測，人工知能学会 言語・音声理解と対話処理研究会（SLUD）第13回対話システムシンポジウム「ポスターセッション」，Dec. 2022

松浦瑠希，鈴木駿吾，発話産出モデルに基づく第二言語発話の流暢性自動採点，第13回横幹連合コンファレンス，Dec. 2022

松浦瑠希，鈴木駿吾，佐伯真於，小川哲司，松山洋一，言い淀みとポーズ位置検出に基づく第二言語発話の流暢性自動採点，日本音響学会2022年春季研究発表会スペシャルセッション「教育支援のための音声処理技術」，Mar. 2022.（学生優秀発表賞受賞）

松山洋一，Tutorial English AI：人と共に成長するオンライン語学学習支援AIシステムの開発，人工知能学会 言語・音声理解と対話処理研究会（SLUD）第12回対話システムシンポジウム「インダストリーセッション」，Nov. 2021.

佐伯真於，鈴木駿吾，松浦瑠希，宮城琴佳，藤江真也，小林哲則，松山洋一，InteLLA：適応的な質問戦略を有するスピーキング能力判定会話エージェント，人工知能学会 言語・音声理解と対話処理研究会（SLUD）第12回対話システムシンポジウム 口頭発表，Nov. 2021.（若手優秀賞受賞）[デモ]

早稲田大学 Tutorial English AI プロジェクト，AI・人工知能EXPO 2021年秋 アカデミックフォーラム，2021年10月．https://www.ai-expo-at.jp/ja-jp/visit/academic-forum.html

“Tutorial English AI プロジェクト“, 早稲田オープン・イノベーション・フォーラム2021 (WOI’21), March 10, 2021. https://waseda-oif21.jp/